Plots - daily and statistics

README.md

@@ -96,8 +96,12 @@ IGLU_PYTHON extends beyond the capabilities of the original IGLU-R package by of
 
 | Function          | Description                              | 
 |-------------------|------------------------------------------|
+| **LOAD DATA FROM DEVICE SPECIFIC FILE**
 | load_libre()      | Load Timeseries from Libre device file (CGM reading converted into mg/dL)
 | load_dexcom()     | Load Timeseries from Dexcom device file (CGM reading converted into mg/dL)
+| **PLOT/VISUALISE CGM **
+| plot_daily()      | Plot daily Glucose values for each day |
+| plot_statistics() | Plot median + quantile daily statistics |
 
 # Installation
 

iglu_python/__init__.py

@@ -10,6 +10,7 @@
 from .ea1c import ea1c
 from .episode_calculation import episode_calculation
 from .extension.load_data import load_dexcom, load_libre
+from .extension.plots import plot_daily, plot_statistics
 from .gmi import gmi
 from .grade import grade
 from .grade_eugly import grade_eugly
@@ -85,6 +86,8 @@
     "median_glu",
     "modd",
     "pgs",
+    "plot_daily",
+    "plot_statistics",
     "process_data",
     "quantile_glu",
     "range_glu",

iglu_python/extension/plots.py

@@ -0,0 +1,148 @@
+"""
+This module implements various plots for the iglu_python package.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+def plot_daily(cgm_timeseries: pd.Series, lower: int = 70, upper: int = 140) -> plt.Figure:
+    """
+    Plot daily Glucose values for each day separately
+
+
+
+    Args:
+        - cgm_timeseries: pd.Series
+        - lower: int, default=70, Lower bound used for hypoglycemia cutoff, in mg/dL
+        - upper: int, default=140, Upper bound used for hyperglycemia cutoff, in mg/dL
+
+    Returns:
+        plt.Figure object
+    """
+    # divide cgm_timeseries into list of daily series
+    cgm_daily_group = cgm_timeseries.resample("D")
+    cgm_timeseries_daily = {day: cgm_daily_group.get_group(day) for day in cgm_daily_group.groups}
+
+    # plot each day separately
+    # Create one figure with subplots for each day
+    num_days = len(cgm_timeseries_daily)
+    fig, axes = plt.subplots(num_days, 1, figsize=(12, 3 * num_days))
+
+    # If only one day, axes will be a single object, not an array
+    if num_days == 1:
+        axes = [axes]
+
+    for i, (day, cgm_one_day) in enumerate(cgm_timeseries_daily.items()):
+        # Convert datetime index to time-only for x-axis display
+        axes[i].plot(cgm_one_day.index, cgm_one_day.values)
+        axes[i].set_title(f"Day: {day.strftime('%Y-%m-%d')}")
+        axes[i].set_ylabel("Glucose (mg/dL)")
+        axes[i].set_ylim(0, max(np.nanmax(cgm_one_day.values), 300))
+
+        # Fill area above upper limit and plot it in orange
+        upper_array = [upper] * len(cgm_one_day.values)
+        area_over_upper = [
+            cgm_one_day.values[i] if cgm_one_day.values[i] > upper else upper for i in range(len(cgm_one_day.values))
+        ]
+        axes[i].fill_between(cgm_one_day.index, area_over_upper, upper_array, alpha=0.3, color="orange")
+        axes[i].axhline(y=upper, color="orange", linestyle="--", alpha=0.7, label=f"Hyper threshold ({upper} mg/dL)")
+
+        # Fill area below lower  limit and plot it in blue
+        lower_array = [lower] * len(cgm_one_day.values)
+        area_below_lower = [
+            cgm_one_day.values[i] if cgm_one_day.values[i] < lower else lower for i in range(len(cgm_one_day.values))
+        ]
+        axes[i].fill_between(cgm_one_day.index, lower_array, area_below_lower, alpha=0.3, color="blue")
+        axes[i].axhline(y=lower, color="blue", linestyle="--", alpha=0.7, label=f"Hypo threshold ({lower} mg/dL)")
+
+        # on horisontal axis, show only time in hours
+        axes[i].set_xlabel("Time (hours)")
+        time_range = pd.date_range(start=day, periods=24, freq="1h")
+        axes[i].set_xticks(time_range)  # Show every hour from 0 to 24
+        axes[i].set_xticklabels([f"{h.hour}" for h in time_range])  # Format as HH:00
+        axes[i].grid(True, alpha=0.3, linestyle="--")
+        axes[i].legend()
+
+    fig.tight_layout()
+    return fig
+
+
+def plot_statistics(cgm_timeseries: pd.Series, lower: int = 70, upper: int = 140) -> plt.Figure:
+    """
+    Plot statistical representation of daily trends
+    in the single 24h timeline, this will plot mean sample trends, 10%, +25% and 75% and 90% quantiles
+    """
+    # check if cgm_timeseries is a pandas series
+    if not isinstance(cgm_timeseries, pd.Series):
+        raise AttributeError("cgm_timeseries must be a pandas series")
+
+    # check if cgm_timeseries is not a datetime index
+    if not isinstance(cgm_timeseries.index, pd.DatetimeIndex):
+        raise AttributeError("cgm_timeseries must have a datetime index")
+
+    # check if cgm_timeseries is not empty
+    if len(cgm_timeseries) < 16:
+        raise ValueError("cgm_timeseries is too short to plot statistics")
+
+    # get sampling frequency
+    time_diffs = cgm_timeseries.index.to_series().diff()
+    dt0 = int(time_diffs.mode().iloc[0].total_seconds() / 60)
+
+    # Create time grid
+    start_time = cgm_timeseries.index.min().floor("D")
+    end_time = cgm_timeseries.index.max().ceil("D")
+    time_grid = pd.date_range(start=start_time, end=end_time, freq=f"{dt0}min")
+    # remove the last time point
+    time_grid = time_grid[:-1]
+
+    # interpolate
+    cgm_timeseries_interpolated = np.interp(
+        (time_grid - start_time).total_seconds() / 60,
+        (cgm_timeseries.index - start_time).total_seconds() / 60,
+        cgm_timeseries.values,
+        left=np.nan,
+        right=np.nan,
+    )
+
+    # reorganise as 2d array with rows as timepoints and columns as days
+    # Reshape to days
+    n_days = (end_time - start_time).days
+    n_points_per_day = 24 * 60 // dt0
+    cgm_timeseries_2d = cgm_timeseries_interpolated.reshape(n_days, n_points_per_day)
+
+    # one day time grid
+    time_grid_one_day = time_grid[0:n_points_per_day]
+    # get mean sample trends
+    mean_sample_trends = np.nanmean(cgm_timeseries_2d, axis=0)
+
+    # get 10%, +25% and 75% and 90% quantiles
+    quantiles = np.nanpercentile(cgm_timeseries_2d, [10, 25, 75, 90], axis=0)
+
+    # create figure and axes
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    # plot mean sample trends
+    ax.plot(time_grid_one_day, mean_sample_trends, color="orange", alpha=1, linewidth=3, label="Mean sample trends")
+
+    # plot quantiles
+    ax.fill_between(time_grid_one_day, quantiles[0], quantiles[1], alpha=0.25, color="blue", label="10% quantile")
+    ax.fill_between(time_grid_one_day, quantiles[1], mean_sample_trends, alpha=0.50, color="blue", label="25% quantile")
+    ax.fill_between(time_grid_one_day, mean_sample_trends, quantiles[2], alpha=0.50, color="blue", label="75% quantile")
+    ax.fill_between(time_grid_one_day, quantiles[2], quantiles[3], alpha=0.25, color="blue", label="90% quantile")
+
+    ax.axhline(y=upper, color="orange", linestyle="--", alpha=0.7, label=f"Hyper threshold ({upper} mg/dL)")
+    ax.axhline(y=lower, color="green", linestyle="--", alpha=0.7, label=f"Hypo threshold ({lower} mg/dL)")
+
+    ax.set_ylim(min(30, np.nanmin(cgm_timeseries.values)), max(np.nanmax(cgm_timeseries.values), 300))
+    ax.set_xlabel("Time (hours)")
+    time_grid_one_day = pd.date_range(start=start_time, periods=24, freq="1h")
+    ax.set_xticks(time_grid_one_day)  # Show every hour from 0 to 24
+    ax.set_xticklabels([f"{h.hour}" for h in time_grid_one_day])  # Format as HH:00
+    ax.grid(True, alpha=0.3, linestyle="--")
+    ax.legend()
+    fig.tight_layout()
+
+    # plot the results
+    return fig

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "iglu_python"
-version = "0.2.4"
+version = "0.2.5"
 description = "Python implementation of the iglu package for continuous glucose monitoring data analysis"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -27,7 +27,8 @@ dependencies = [
     "numpy>=2.2.6",
     "pandas>=2.2.3",
     "tzlocal>=5.3.1",
-    "openpyxl >= 3.1.5"
+    "openpyxl >= 3.1.5",
+    "matplotlib >= 3.10.0"
 ]
 
 [project.urls]

requirements.txt

@@ -1,4 +1,5 @@
 pandas >= 2.2.3
 numpy >= 2.2.6
 tzlocal >= 5.3.1
-openpyxl >= 3.1.5
+openpyxl >= 3.1.5
+matplotlib >= 3.10.0